• Publications
  • Influence
Optical Performance Monitoring: A Review of Current and Future Technologies
TLDR
Optical performance monitoring (OPM) is the estimation and acquisition of different physical parameters of transmitted signals and various components of an optical network. Expand
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  • 11
Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural networks.
TLDR
We experimentally demonstrate the use of deep neural networks (DNNs) in combination with signals' amplitude histograms (AHs) for simultaneous optical signal-to-noise ratio monitoring and modulation format identification (MFI) in digital coherent receivers. Expand
  • 91
  • 6
Modulation Format Identification in Coherent Receivers Using Deep Machine Learning
TLDR
We propose a novel technique for modulation format identification (MFI) in digital coherent receivers by applying deep neural network (DNN) based pattern recognition on signals' amplitude histograms obtained after constant modulus algorithm (CMA) equalization. Expand
  • 73
  • 3
Simultaneous optical performance monitoring and modulation format/bit-rate identification using principal component analysis
TLDR
We propose a novel technique for simultaneous multi-impairment monitoring and autonomous bit-rate and modulation format identification in next-generation heterogeneous fiber-optic communication networks by using principal component analysis-based pattern recognition on asynchronous delay-tap plots. Expand
  • 60
  • 3
Field trial of Machine-Learning-assisted and SDN-based Optical Network Planning with Network-Scale Monitoring Database
TLDR
An SDN based network planning framework utilizing machine-learning techniques and a network-scale monitoring database is implemented over an optical field-trial testbed comprised of 436.4km fibre. Expand
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  • 2
  • PDF
Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks.
TLDR
We propose a simple and cost-effective technique for modulation format identification (MFI) in next-generation heterogeneous fiber-optic networks using an artificial neural network (ANN) trained with the features extracted from the asynchronous amplitude histograms (AAHs). Expand
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  • 1
  • PDF
An Optical Communication's Perspective on Machine Learning and Its Applications
TLDR
Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. Expand
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Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling.
  • F. Khan, Yi Yu, +4 authors C. Lu
  • Computer Science, Medicine
  • Optics express
  • 16 November 2015
TLDR
We experimentally demonstrate simultaneous optical signal-to-noise ratio (OSNR) monitoring and modulation format identification in heterogeneous fiber-optic networks by using principal component analysis (PCA) and statistical distance measurement based pattern recognition on scatter plots obtained through asynchronous single channel sampling. Expand
  • 27
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Joint modulation format/bit-rate classification and signal-to-noise ratio estimation in multipath fading channels using deep machine learning
TLDR
A novel algorithm for simultaneous modulation format/bit-rate classification and non-data-aided (NDA) signal-to-noise ratio estimation in multipath fading channels by applying deep machine learning-based pattern recognition on signals’ asynchronous delay-tap plots . Expand
  • 21
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Non-data-aided joint bit-rate and modulation format identification for next-generation heterogeneous optical networks
TLDR
A novel and cost-effective technique for simultaneous bit-rate and modulation format identification (BR-MFI) in next-generation heterogeneous optical networks without requiring any prior information from the transmitters. Expand
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